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Record W1995389056 · doi:10.1149/1.1393289

Energy and Capacity Projections for Practical Dual-Graphite Cells

2000· article· en· W1995389056 on OpenAlexafffund
J. R. Dahn, J. A. Seel

Bibliographic record

VenueJournal of The Electrochemical Society · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsElectrolyteGraphiteIntercalation (chemistry)ElectrodeElectrochemistryIonSalt (chemistry)Materials scienceDual (grammatical number)Electrochemical cellChemistryChemical engineeringChemical physicsInorganic chemistryComposite materialPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Anion intercalation in graphite was investigated in the preceding paper. It was shown that about 140 mAh/g, corresponding to stage‐two' , of charge could be stored at potentials near 5 V vs. Li. Dual‐graphite cells, where Li intercalates into the negative electrode and into the positive electrode, are therefore possible, as has been demonstrated before by others. Unlike a Li‐ion cell, where the amount of electrolyte in the cell can be minimized to obtain high energy density, in a dual‐graphite cell there must be enough electrolyte present to provide the ions needed during the charging of the cell. This leads to a strategy in cell design that differs from the Li‐ion case, in that electrolytes with high salt concentration are crucial if high energy density is to be obtained. Here, the specific capacity, specific energy, volumetric capacity, and energy density of practical dual‐graphite cells are predicted. The calculations focus on the effect of the maximum salt concentration difference in the electrolyte between the fully discharged and fully charged states. © 2000 The Electrochemical Society. All rights reserved.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.239
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations193
Published2000
Admission routes2
Has abstractyes

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